File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/TIP.2010.2044958
- Scopus: eid_2-s2.0-77953705810
- WOS: WOS:000278813800020
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction
Title | Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction |
---|---|
Authors | |
Keywords | Dimension reduction Face recognition Manifold embedding Semi-supervised learning |
Issue Date | 2010 |
Citation | IEEE Transactions on Image Processing, 2010, v. 19, n. 7, p. 1921-1932 How to Cite? |
Abstract | We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F 0 = F - h (X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F0. Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X)and F, we show that FME relaxes the hard linear constraint F = h (X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms. © 2006 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/321406 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nie, Feiping | - |
dc.contributor.author | Xu, Dong | - |
dc.contributor.author | Tsang, Ivor Wai Hung | - |
dc.contributor.author | Zhang, Changshui | - |
dc.date.accessioned | 2022-11-03T02:18:42Z | - |
dc.date.available | 2022-11-03T02:18:42Z | - |
dc.date.issued | 2010 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2010, v. 19, n. 7, p. 1921-1932 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/321406 | - |
dc.description.abstract | We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points. For semi-supervised dimension reduction, we aim to find the optimal prediction labels F for all the training samples X, the linear regression function h(X) and the regression residue F 0 = F - h (X) simultaneously. Our new objective function integrates two terms related to label fitness and manifold smoothness as well as a flexible penalty term defined on the residue F0. Our Semi-Supervised learning framework, referred to as flexible manifold embedding (FME), can effectively utilize label information from labeled data as well as a manifold structure from both labeled and unlabeled data. By modeling the mismatch between h(X)and F, we show that FME relaxes the hard linear constraint F = h (X) in manifold regularization (MR), making it better cope with the data sampled from a nonlinear manifold. In addition, we propose a simplified version (referred to as FME/U) for unsupervised dimension reduction. We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques. Comprehensive experiments on several benchmark databases demonstrate the significant improvement over existing dimension reduction algorithms. © 2006 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | Dimension reduction | - |
dc.subject | Face recognition | - |
dc.subject | Manifold embedding | - |
dc.subject | Semi-supervised learning | - |
dc.title | Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2010.2044958 | - |
dc.identifier.scopus | eid_2-s2.0-77953705810 | - |
dc.identifier.volume | 19 | - |
dc.identifier.issue | 7 | - |
dc.identifier.spage | 1921 | - |
dc.identifier.epage | 1932 | - |
dc.identifier.isi | WOS:000278813800020 | - |